The issue of preserving sensitive information in medical datasets while maintaining their utility is a significant concern in the implementation of Differential Privacy (DP). This paper presents a framework for allocating privacy budgets, designed to optimize the total budget for medical datasets and thereby enhance data utility. Previous strategies for allocating privacy budgets have primarily relied on fixed mathematical rules, and excessive or insufficient noise addition can impact data utility. Therefore, we propose a Genetic Algorithm (GA)-based framework that generates a privacy budget sequence through selection, crossover, and mutation operations to arrive at an attribute-wise optimal privacy budget. After that, the same individual optimal budget is utilized for each record in publishing, ensuring individual privacy guarantees. Experimental findings on two medical datasets reveal enhanced data utility when compared to heuristic budget allocation methods. This framework presents a straightforward and efficacious strategy for allocating privacy budgets within the context of privacy-preserving medical data publication. The source code used in this study is publicly available at https://github.com/Wayne-on-the-road/OPBA-MDP .